The AI-Native Developer Workflow: Prompt, Agent, Review, Test
Technical trends, engineering practices, and an independent-developer perspective around AI and AI agents.
This aggregates CodeNodeX AI community articles and discussions related to AI, covering concept explanations, tool comparisons, engineering practices, common issues, and indie hacking experience.
If you're researching AI, start with high-quality long-form articles, then join post discussions for a complete understanding-to-practice path.
Technical trends, engineering practices, and an independent-developer perspective around AI and AI agents.
Technical trends, engineering practices, and an independent developer's perspective on DevOps and AI.
Technical trends, engineering practices, and independent developer perspectives around AI Agents and AI.
Centered on the technical trends, engineering practices, and indie developer perspectives of indie development and AI.
Centered on Vibe Coding, AI technology trends, engineering practices, and the independent developer perspective.
A prompt is not a "wish" but a specification for the model. This article explains the six elements of a structured prompt, advanced techniques such as CoT, few-shot examples, and XML segmentation, defensive phrasing, and a reusable debugging method that turns intuition-driven prompt tuning into an engineering process.
A high-quality prompt is not a magic instruction but an engineering system comprising a task contract, context, examples, an output structure, and an evaluation set. This article presents a reusable prompt design method and explains how version control and regression testing enable stable iteration.
"Model leaderboards cannot replace business evaluation. This article designs a five-layer LLM application evaluation framework spanning evaluation-set construction, rule-based metrics, model judges, human review, and production observation to support model selection, Prompt regression, and RAG optimization."
A single Agent can confidently go off course on complex tasks. This article explains three multi-Agent orchestration patterns (supervisor, hierarchical, and handoff), implements a "supervisor + specialists" system with LangGraph's state graph, and covers the state, loop, and human-intervention engineering needed for production.
Deleting one row in the primary table won't clear vectors, summaries, caches, or restored backups. This article uses a deletion state machine, generational tombstones, derived lineage, and end-to-end canary verification to truly forget.
Not every LLM task needs to run in real time. Batch classification, summarization, labeling, and content generation can cost half as much with Batch APIs. This article covers OpenAI and Anthropic Batch APIs, self-hosted batching with vLLM, job pipelines, and real-time vs. batch tradeoffs.
Preferences and facts a model extracts from a conversation cannot become long-term memory directly. This article designs candidate proposals, deterministic validation, deduplicated commits, revocation lineage, and a contamination-rate gate.